Zulu5 delivers trustworthy, data-driven insights into online advertising activity.
By continuously observing thousands of publisher domains and analyzing the ads that are actually visible to users, Zulu5 provides an independent and consistent view of digital advertising patterns across markets:
By continuously observing thousands of publisher domains and analyzing the ads that are actually visible to users, Zulu5 provides an independent and consistent view of digital advertising patterns across markets:
- Advertisers active on specific websites and channels
- Frequency and placement of creatives
- Estimated ad impressions and media spend
- Market trends across industries, formats, and countries
Zulu5 doesn’t claim exact numbers — it provides consistent, comparable data that reveals patterns and competitive trends across digital markets.
This article sheds light on Zulu5’s methodology of data collection, estimation scaling, and on validation mechanisms of data quality.
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A Methodology Built on Transparency
Synthetic User SimulationAt the core of Zulu5 lies a proprietary crawler that behaves like a synthetic user, simulating authentic browsing sessions — scrolling, moving the mouse, interacting with cookie banners, and triggering ads as real users would.Comprehensive Ad-Tech VisibilityZulu5 detects and classifies every ad format it encounters — display, video, native, or programmatic — capturing:- The creative and landing page
- Placement and size
- The supply-side ad-tech chain (e.g., SSPs and exchanges visible through the page or header bidding)
Zulu5 focuses on supply-side transparency. While DSP activity occurs server-side and is not directly visible, patterns in creatives, trackers, and auction responses provide directional insights into demand-side buying behavior.Consistent and Scalable CoverageZulu5’s crawlers operate in Kubernetes clusters, dynamically scaling to maintain consistent coverage across markets. Crawling frequency reflects both domain relevance and traffic potential, ensuring stable, representative sampling across thousands of publishers in every supported market. -
From Observation to Reliable Estimation
Zulu5’s methodology follows a transparent, three-step logic:- Share of Voice (SoV) – Zulu5 measures the share of visible ads per advertiser on each publisher domain.
- Estimated Ad Impressions – SoV values are scaled using licensed traffic indicators from SimilarWeb (e.g., visits and average impressions per visit), providing a realistic weighting factor that translates observed activity into estimated impressions.
- Estimated Media Spend – Impressions are then multiplied by market-specific CPM benchmarks from verified data sources such as Audienzz.com and other audited price lists.
This layered approach allows clients to work with reliable indicators such as relative SoV, estimated impressions, or estimated spend, depending on their local market practices and reporting standards. -
Continuous Validation and Improvement
Zulu5 continuously validates and refines its methodology through independent audits and data-science partnerships. These validation studies compare Zulu5’s modeled impressions and spend with verified advertiser and publisher data, identifying and correcting over- or underestimation factors.Continuous Validation and Improvement- Synthetic user simulations to better represent real browsing diversity
- Improved cookie handling to account for consent-based variations
- Adaptive queueing for optimized resource allocation and balanced market coverage
This process ensures Zulu5’s estimates remain methodologically sound, traceable, and consistent over time. -
Why Customers Trust Zulu5
- Consistent Over Time – A stable, repeatable methodology enabling long-term benchmarking across markets.
- Transparent and Explainable – Every metric is traceable from observed data to derived estimate.
- Independent by Design – 100% neutral and GDPR-compliant data collection with no platform or vendor bias.
- Validated by Experts – Methodology verified through independent data science evaluations.
- Flexible for Local Use Cases – Supports markets that operate with SoV-, impression-, or spend-based KPIs.
Curious?
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